Mastering AI Product Strategy: A Blueprint for Success
Mastering AI Product Strategy: A Blueprint for Success
Product Managers unlock the power of AI by defining a futuristic product strategy.
In today's digital landscape, where AI products reign supreme, a group of unsung heroes is orchestrating the symphony of innovation behind the scenes. Enter the Artificial Intelligence Product Manager (AI PM), the mastermind who seamlessly blends business acumen, technical prowess, and customer-centric thinking to craft award-winning product strategies. These T-shaped individuals are the driving force behind companies that produce AI products, guiding their organizations to embrace the boundless potential of AI while ensuring fair and responsible practices.
In the age of Gen AI, you hear about the glamorous lives of Data Scientists and Data Engineers, but what makes AI Product Managers so special? How do they build AI products without writing a single line of code? Why is Netflix advertising for an AI PM job paying up to 900k?! And why is the AI Product Manager role even needed?
The short answer is AI Product Managers craft award-winning product strategies that paint a compelling proposition for customers to adopt AI products while raising the margins of the business; Along the way, AI PMs ensure these solutions do this in a manner that minimizes existential risk; all within the bounds of the tech’s limitations.
In this article, we’ll dive into the main points that AI Product Managers should consider when crafting an AI product strategy. Here’s the blueprint:
Unlocking Business Value with AI
Understanding AI’s Limitations
The Ethical AI Framework
Criteria for Evaluating Prime AI Use Cases
Let’s get started!
Understanding AI
Before we dive headfirst into AI product strategy, let's establish a clear understanding of what artificial intelligence, truly entails. At its core, AI encompasses the ability of computers to execute tasks that generally require human-like intelligence, including cognizance, inference, research, and determination.
Over time, AI has undergone distinct evolutionary phases. In the early 2000s, the emergence of Machine Learning introduced statistical algorithms that enabled computers to learn from data, progressively enhancing their performance.
In more recent advancements, Deep Learning harnessed the power of artificial neural networks to analyze unstructured datasets such as images, text, and videos. This approach has paved the way for Gen AI applications, such as large language models (LLMs) like ChatGPT.
As AI continues to evolve, its potential to shape society and the economy is poised to grow even more profoundly in the future. And that’s where the role of an AI Product Manager truly comes into focus.
Crafting an Award-winning AI Product Strategy
Due to rapid advancements in technology, AI is finding its way into a plethora of business applications. Now there are even more ways to start unlocking its benefits. AI PMs who are successful work tirelessly to craft well-architected strategic frameworks. There are four main tenet of a robust AI product strategy:
Firstly, there’s the product vision. Connecting AI product objectives back to the business ensures the product truly supports enterprise initiatives. It’d be prudent to have accurately defined North Star success metrics that tie to business KPIs; it’s a crucial step to understand how AI plays a role in that vision. Product Managers evaluate the potential costs vs. the value that AI creates during business case development.
Secondly, gauging the organization’s appetite and exposure to risk and having a mitigation plan is absolutely critical. We’ll elaborate on this more in the “Ethical AI” section.
Thirdly, AI Product Managers ensure that change management plans are implemented since AI products inevitably lead to transformations in systems, people, and processes.
Lastly, it’s essential to secure buy-in from stakeholders. AI implementations have a lot of moving pieces that involve coordination and collaboration with many different teams. These teams should all be in lock-step, in addition to touting the product benefits to their peers and leadership.
By embracing AI, organizations can unlock creativity, enhance productivity, and streamline efficiency across the board, ushering in a new era of growth and value creation.
Unlocking Business Value with AI
Like with any shiny new piece of tech, AI can create immense value for enterprise organizations when it’s used correctly; but it also comes with clear limitations that stakeholders need to be aware of. AI Product Managers need to have a crisp POV on the value proposition, but there’s also a need to communicate the boundaries that come with the territory. Risk mitigation plans need to be created before AI products are fully launched in production.
Integrating AI into critical business operations has quickly shifted from a luxury to a necessity, in order to maintain a competitive edge in the market. In the age of Gen AI, this becomes more apparent.
There are three primary sources of value that AI can bring to businesses:
Efficiency
Productivity
Creativity
AI's efficiency gains are characterized by automation, data synthesis, event identification, and resource optimization. AI can help simplify and streamline tasks that become too time-consuming. For example, AI can quickly summarize long email threads, and provide clear action items, as well as takeaways. Streamlining operations not only saves substantial time and effort but also jumpstarts the journey of adding tangible business value with AI. Automating mundane tasks becomes a cornerstone for efficiency.
Efficiency is all about streamlining processes, while productivity is all about raising the bar on outputs. Time-savings, whether partial or complete, enable employees to achieve more within the same timeframe. Now they can work on higher-value initiatives with this newly found capacity. For example, consider an AI model that prioritizes new sales leads based on their buying propensity; salespeople can now focus on closing deals instead of sifting through a neverending sea of leads. Sounds like a win-win.
Creativity has become another cornerstone for unlocking value with AI. AI can facilitate creativity as professionals learn how to incorporate Gen AI into their workflows. For example, AI can automate repetitive tasks, like drafting marketing materials. By eliminating some of the upfront prep work, AI can facilitate creativity even faster.
Understanding AI’s Limitations
Another component of the blueprint is understanding AI’s limitations.
These limitations can span over a handful of categories:
Comprehension: AI products give the illusion of regurgitating context through their responses, but in reality, they rely heavily on probabilities to make determinations. While it may seem like AI can opine on optimal eCom strategies — for example — its responses lack the cognitive intuition that’s required for a complete comprehension of context. Common sense is replaced by statistical commonalities, potentially yielding unintended responses.
Subjective Judgement & Ethics: AI grapples with challenges of subjective judgment, and ethical dilemmas while balancing competing priorities. It can calculate probabilities but navigating complex moral dilemmas is currently beyond its capacity.
Critical Thinking: AI's prowess in devising novel strategies is also quite limited. While Gen AI can produce creative content in images, sounds, and text, it relies heavily on human-provided prompts and feedback for refinement.
Explainability: Certain AI models function as opaque "black boxes," hindering human interpretability of its decision-making processes. Explainable AI requires enhancing its capabilities to shed light on the rationale behind its decisions.
Adaptive Dexterity: AI also lacks human-like dexterity, partly due to constraints in robotic technology.
These limitations, especially adaptability and comprehension, often stem from insufficient data quality and training volume. Building effective AI models requires copious amounts of data, especially for diverse use cases. Incomplete data can lead to biased outcomes. Moreover, encoding complex situations in a generalizable and scalable manner is a modern challenge for AI's continuous learning capabilities.
Although technological advancements have already begun to mitigate some of these limitations, the majority of AI systems will likely necessitate a "human-in-the-loop" design. Human integration can compensate for AI's deficiencies. A decent example of this is prompt engineering within Chat GPT.
The Ethical AI Framework
AI has the potential to bring tremendous and positive impact on our lives. However, like many newer technologies, AI can also cause unintended consequences when it’s implemented or used incorrectly. Responsible AI PMs place a lot of focus on governance via the ethical AI framework.
The ethical AI framework is a method of building, evaluating, and deploying AI products in a secure, reliable, and responsible manner. Companies might create their own frameworks, depending on their business; but it’s normally grounded on morality, lawfulness, and societal effects.
There are several important tenants in the ethical AI framework, including:
Accountability: who assumes ownership if something goes awry?
Inclusivity: making sure diverse viewpoints are considered, including external ones outside of the company.
Fairness: not supporting detrimental biases.
Explainability: providing insights into why an AI product provided a specific recommendation, decision, or response.
Security: ensuring the data, the model, the system, and the users are secure when AI products are operational.
Reliability: results and interactions are relatively consistent when the AI product is used on a recurring basis.
Each tenant needs to be well-defined and it should be clear how each will be evaluated. In addition, they should be considered at every stage of the AI product build - including data collection, design, and development.
With more investment into AI, a business may need to establish an AI ethics committee; especially when making determinations on whether to scale up.
Risk & Impact Assessments
AI Product Managers should commence risk and impact assessments as soon as possible. Impact assessments are an integral component of the ethical AI framework that serves as the precursor to risk mitigation plans. Impact assessments help PMs evaluate the potential effects that AI has on its end-users as well as other stakeholders, companies, and society at large. Impact assessments typically correlate to the ethical AI tenants.
Risk assessments are mandatory checklists that AI Product Managers utilize to answer basic questions on the product’s purpose, its end-users, and its intended use case(s) to help identify possible risks. These questions include:
Who will use or be exposed to the AI product?
What types of decisions will the AI product make?
What entities will the decisions be about?
What type of data or other technologies are being used within the AI product?
There are plenty of resources with sufficient framing and foundations to help AI PMs conduct fair and honest assessments.
Criteria for Evaluating Prime AI Use Cases
After reviewing AI’s benefits and limitations, as well as our responsibility as AI Product Managers, let’s take a look at how AI PMs can determine whether a use case is ripe for AI.
During the impact assessment, it’s exciting for AI Product Managers to dive into the specifics of the who, when, and what, but it's also important to understand why.
Why should AI be used for this problem?
Why will it improve the customer experience?
Why will the solution be important to the business?
Why now?
Having a crisp understanding of why a problem needs to be solved and why AI is the solution will make for a stronger AI product use case. Furthermore, defining the problem statement will also help in evaluating the rest of the criteria. When considering whether business problems can be solved with AI, the following factors need to be considered:
Augment employees, don't use AI to replace them. AI is designed to work alongside humans - not replace them. AI should augment and improve our mere mortal abilities.
Make sure sufficient data is available. AI products require large amounts of data for training purposes. In addition, data quality must be sufficient for the use case.
Most job functions have some aspects where AI could be applied, especially when the function can be decoupled into smaller tasks. After identifying a potential use case, AI Product Managers can deconstruct complex processes into smaller pieces of functionality. This helps define which tasks are suitable for AI, such as predicting an outcome, ranking leads, summarizing an email, or creating content.
AI PMs should keep these tenets in mind, as there are some initiatives that can be solved without the use of AI. There’s no need to build a complicated AI product to accomplish something that’s simple — like using ChatGPT for spell-check.
Here are some other questions PMs can ask while evaluating whether a use case is suitable for AI:
How are industry competitors using AI?
What about partners in different industries?
What departments demonstrate a clear need for support?
Where is there enough funding to support a small proof-of-concept (POC)?
Once an AI use case passes all of these tests, the next step is to build a POC. It’d be prudent to start with well-defined MVP scope and scale once the POC is proven to deliver ROI. A POC is a great way to iteratively test and improve the capability until it’s robust enough to scale.
AI Leadership with Precision & Purpose
As Gen AI continues its meteoric rise, AI Product Managers stand at the helm, steering companies toward success in a digital landscape defined by innovation and disruption. Their ability to craft strategic blueprints that meld business value, technological feasibility, and ethical considerations is what sets them apart. From defining the product vision to navigating the intricacies of ethical AI, these unsung heroes sculpt the future of AI products in a good way.
So, whether you're an aspiring AI Product Manager or a business leader seeking to harness the transformative power of AI, remember this: crafting an award-winning AI product strategy isn't just a task, it's an artform, and AI PMs are the virtuosos shaping tomorrow's possibilities.
Sources
Implementing AI Solutions in Business | DataCamp